More by Tilmann Gneiting

Abstract

In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional forecast evaluation methods to subsets of extreme observations has unexpected and undesired effects, and is bound to discredit skillful forecasts when the signal-to-noise ratio in the data generating process is low. Conditioning on outcomes is incompatible with the theoretical assumptions of established forecast evaluation methods, thereby confronting forecasters with what we refer to as the forecaster’s dilemma. For probabilistic forecasts, proper weighted scoring rules have been proposed as decision-theoretically justifiable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a real data study on probabilistic forecasts of U.S. inflation and gross domestic product (GDP) growth, we illustrate and discuss the forecaster’s dilemma along with potential remedies.

Bernoulli, J. (1713). Ars Conjectandi. Impensis Thurnisiorum, Basileae. Reproduction of original from Sterling Memorial Library, Yale University. Online edition of Gale Digital Collections: The Making of the Modern World: Part I: The Goldsmiths’-Kress Collection, 1450–1850. Available at http://nbn-resolving.de/urn:nbn:de:gbv:3:1-146753.

Bernoulli, J. (2006). The Art of Conjecturing: Together with “Letter to a friend on sets in court tennis”, Translated from the Latin and with an introduction and notes by Edith Dudley Sylla. Johns Hopkins Univ. Press, Baltimore, MD.